{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Motivating examples\n",
    "This document shows how SparseSC can be used in places where simple difference-in-differences can't."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "#%matplotlib inline\n",
    "\n",
    "try:\n",
    "    import SparseSC as SC\n",
    "except ImportError:\n",
    "    raise RuntimeError(\"SparseSC is not installed. use 'pip install -e .' from repo root to install in dev mode\")\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, \".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import example_graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make sure the results are the same each time we run\n",
    "random.seed(12345) \n",
    "np.random.seed(12345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup common data parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "N1,N0_sim,N0_not = 0,50,50\n",
    "N0 = N0_sim + N0_not\n",
    "N=N1+N0\n",
    "treated_units = range(N1)\n",
    "control_units = list(set(range(N)) - set(treated_units))\n",
    "T0,T1 = 5, 5\n",
    "T=T0+T1\n",
    "unit_treatment_periods = np.full((N), -1)\n",
    "unit_treatment_periods[treated_units] = T0\n",
    "proto_te = np.hstack((np.zeros((1,T0)), np.full((1,T0), 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differential Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "proto_sim = np.array(range(0,T,1),ndmin=2)\n",
    "proto_not = np.array(range(0,2*T,2),ndmin=2)\n",
    "proto_tr  = proto_sim + proto_te\n",
    "Y1 = np.matmul(np.ones((N1,1)), proto_tr)\n",
    "Y0_sim = np.matmul(np.ones((N0_sim,1)), proto_sim) + \\\n",
    "    np.hstack((np.zeros((N0_sim,T0)),np.random.normal(0,.4,(N0_sim,T1))))\n",
    "Y0_not = np.matmul(np.ones((N0_not,1)), proto_not)+ \\\n",
    "    np.hstack((np.zeros((N0_sim,T0)),np.random.normal(0,.4,(N0_sim,T1))))\n",
    "Y = np.vstack((Y1,Y0_sim,Y0_not))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Motivation: Treated unit does not look like a simple average of controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x267f490edd8>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "example_graphs.raw(Y, treated_units, control_units, T0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "unit_treatment_periods = np.full((N), np.nan)\n",
    "##unit_treatment_periods[treated_units = T0\n",
    "X_and_Y_pre = Y[:,:5]\n",
    "Y_post = Y[:,5:]\n",
    "#fit_sc = SC.fit(X_and_Y_pre, Y_post, model_type=\"full\", constrain=\"simplex\",\n",
    "#                              print_path = False, progress = False, verbose=0) #Do fast\n",
    "fit_sc = SC.fit_fast(X_and_Y_pre, Y_post, model_type=\"full\") #Do fast\n",
    "#simple_summ(ret, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x267f4cde358>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_sc = fit_sc.predict(Y)\n",
    "diffs = Y - Y_sc\n",
    "example_graphs.diffs_plot(diffs, [], control_units, T0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 432x288 with 1 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x267f91f20f0>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from SparseSC.utils.metrics_utils import AA_results\n",
    "aa_res = AA_results(diffs[:,:T0], diffs[:,T0:])\n",
    "example_graphs.te_plot(aa_res, T0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
